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SMO Lattices for the Parallel Training of Support Vector Machines
2015
The European Symposium on Artificial Neural Networks
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In this work, a method is proposed to train Support Vector Machines in parallel. ...
Experimental validation demonstrates the advantages in terms of speed in comparison to other approaches. ...
In recent years, many classification algorithms have been proposed but still one of the most popular and most widely used classifiers is the Support Vector Machine (SVM) [2] . ...
dblp:conf/esann/KachelePS15
fatcat:wfqz6x2j6rch5n2ukcg27b6gdy
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
2006
BMC Bioinformatics
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Results: In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition method for support vector machines ( ...
Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. ...
Acknowledgements The authors are grateful to the Mississippi Center for Supercomputing Research (MCSR) for providing state-of-the-arts high performance computing facilities and excellent services for supporting ...
doi:10.1186/1471-2105-7-s4-s15
pmid:17217507
pmcid:PMC1780126
fatcat:yyj5eykzmje3zb6qu2jbgznxsu
Improved Detecting Host Based Intrusions Based On Hybrid SVM Using Grey Wolf Optimizer
2017
International Journal of Security and Its Applications
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by combining the Support Vector Machine approach from classifier-based techniques and the Grey Wolf Optimizer from evolutionary techniques to optimize the support vector machine parameter towards the accurate ...
Based on the consideration of challenging task and performance existence of contemporary computational methodologies, the objective of this Proposed Research has developed the enhanced hybrid strategy ...
They are known to be Support Vector Machine-based Detection approaches; Optimization algorithm-based Detection approaches and Hybrid approaches of classifier and evolutionary techniques. ...
doi:10.14257/ijsia.2017.11.9.05
fatcat:f3umclojrvc5nocuzb66gfjb4y
Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds
2004
Journal of chemical information and computer sciences
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From the applied machine learning techniques the rule learner PART and support vector machines gave the best results, although the differences between the learning algorithms are only marginal. ...
This paper explores the utility of data mining and machine learning algorithms for the induction of mutagenicity structure-activity relationships (SARs) from noncongeneric data sets. ...
The machine learning algorithms were used with their default settings in the WEKA-Workbench. Support vector machines were used with a linear and quadratic kernel. ...
doi:10.1021/ci034254q
pmid:15272848
fatcat:fxwf2eazqfhnrjqrrpmp4733eu
CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study
2018
Scientific Reports
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Machine learning algorithms selected and combined 1,695 quantitative image features to build prediction models. ...
We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung ...
Acknowledgements This work was supported in part by Grant R01 CA149490 from the National Cancer Institute (NCI). ...
doi:10.1038/s41598-018-36421-0
pmid:30559455
pmcid:PMC6297245
fatcat:tipf6bfai5dhza6eqwzvzom3u4
Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases
2019
International Journal of Advanced Computer Science and Applications
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AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. ...
The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. ...
Support vector machine based classifier like SMO-C, NU_SVM-C and C_SVM-C are used in this proposed thesis on selected datasets. ...
doi:10.14569/ijacsa.2019.0100940
fatcat:ekopo2ll6zgnjdqg4xpqqyz46q
Support vector machine classification for large datasets using decision tree and Fisher linear discriminant
2014
Future generations computer systems
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The training of a support vector machine (SVM) has a time complexity between O(n 2 ) and O(n 3 ). Most training algorithms for SVM are not suitable for large data sets. ...
A decision tree is used to detect low entropy regions in input space. We use Fisher's linear discriminant to detect the data near to support vectors. ...
Support Vector Machine with Decision Tree and Fisher Linear Discriminant According to the geometric properties of SVM, the separating hyperplane of SVM is based on the support vectors which is a small ...
doi:10.1016/j.future.2013.06.021
fatcat:bxchq2piofawzmnb3gudlg7nzi
Metaheuristic Ant Lion and Moth Flame Optimization based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks
2021
IEEE Access
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The team used the support vector machines and worked on a total of 1234K Italian tweets. In addition, they obtained f-score values above 80% [22] . ...
Michele et al. used supervised machine learning algorithms such as support vector machines, neural networks, and logistic regression. ...
doi:10.1109/access.2021.3102277
fatcat:jqn7zuebh5cdzjdlnzkszsaxam
A Stacked Sparse Autoencoder-based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy
2018
IEEE Transactions on Medical Imaging
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Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. ...
After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. ...
ACKNOWLEDGMENT This work was supported by National Key R&D Program of China (Grant No. 2017YFC0113000). ...
doi:10.1109/tmi.2018.2836965
pmid:29994761
pmcid:PMC6299455
fatcat:qfy5mwg3nfhvnn2fdiqxl54cae
A Novel Holistic Disease Prediction Tool Using Best Fit Data Mining Techniques
2017
International Journal of Computing and Digital Systems
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Test results for breast cancer and HIV data sets are reported. ...
As diseases are diagnosed, the predictive tool helps medical doctors in decision-making about what disease case it is and suggests possible treatment strategies within a much-reduced time. ...
d) Sequential Minimum Optimization (SMO) - SMO is a supervised learning algorithm that works the same way as support vector machine (SMV) algorithms that was introduced by Vapnik, et al. [21] . ...
doi:10.12785/ijcds/060202
fatcat:avsekx544zfunenijuaddqamlu
Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach
English
2018
International Journal of Engineering Trends and Technoloy
English
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Fraudulent e-commerce buyers and their transactions are being studied in detail and multiple strategies to control and prevent them are discussed. ...
This paper attempts to suggest a framework to detect such fraudulent sellers with the help of machine learning techniques. ...
Renjith Ranganadhan for the support, guidance, reviews, valuable suggestions and very useful discussions in the domain of retail marketing. ...
doi:10.14445/22315381/ijett-v57p210
fatcat:kb2mzf5nzbhupamnk3ymo6fhoi
Fast Sparse Approximation for Least Squares Support Vector Machine
2007
IEEE Transactions on Neural Networks
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Index Terms-Fast algorithm, greedy algorithm, least squares support vector machine (LS-SVM), sparse approximation. ...
In this paper, we present two fast sparse approximation schemes for least squares support vector machine (LS-SVM), named FSALS-SVM and PFSALS-SVM, to overcome the limitation of LS-SVM that it is not applicable ...
In using a small random set of the training samples as the candidate support vectors, the cost is dropped to , with the size of random set. ...
doi:10.1109/tnn.2006.889500
pmid:17526336
fatcat:evegripr2rgbvpq4ng7fa5dgiy
Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data
2015
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. ...
Over the past years, fuzzy decision tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. ...
Experimentation with a multiclass classifier based on SVM (Support Vector Machine) algorithm is reported in [11] . ...
doi:10.1109/fuzz-ieee.2015.7337854
dblp:conf/fuzzIEEE/LudwigJP15
fatcat:v2t37grxsvdn5e2skqgbipo2jy
MIC-SVM: Designing a Highly Efficient Support Vector Machine for Advanced Modern Multi-core and Many-Core Architectures
2014
2014 IEEE 28th International Parallel and Distributed Processing Symposium
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Support Vector Machine (SVM) are widely used in datamining and big data applications. ...
In recent years, SVM has been used in High Performance Computing (HPC) for power/performance prediction, auto-tuning, and runtime scheduling. ...
ACKNOWLEDGMENT This work was supported in part by National Natural Science Foundation of China (61303003 and 41374113) and National High-tech R&D (863) Program of China (2013AA01A208), and DOEnOSCAR Beyond ...
doi:10.1109/ipdps.2014.88
dblp:conf/ipps/YouSFMDBCRY14
fatcat:vv6jzxoccbek5hz7bgiqhyt3dq
Codeword Selection for Concurrent Transmissions in UAV Networks: A Machine Learning Approach
2020
IEEE Access
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INDEX TERMS Machine learning, UAV networks, concurrent transmissions, codeword selection, support vector machine. ...
On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing ...
The data are further prepared for extracting the features by ML, where the support vector machine (SVM) classifier is used. ...
doi:10.1109/access.2020.2968533
fatcat:al5ulguvvrfzna2c7a42we3lby
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